In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. 
 Question: What cluster identification method is used in this paper?
A combination of Minimum spanning trees, K-Nearest Neighbors and Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18

word embedding, input encoder, alignment, aggregation, and prediction 
 Question: what is the architecture of the baseline model?
word embedding, input encoder, alignment, aggregation, and prediction.

$D_a$ contained all of the tweets collected on the attack day of the five attacks mentioned in section 4.2. And $D_b$ contained all of the tweets collected before the five attacks. There are 1180 tweets in $D_a$ and 7979 tweets in $D_b$. The tweets on the attack days ($D_a$) are manually annotated and only 50 percent of those tweets are actually about a DDoS attack. 
 Question: Do twitter users tend to tweet about the DOS attack when it occurs? How much data supports this assumption?

The dataset contains about 590 tweets about DDos attacks.

